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The use of automatic grading tools has become nearly ubiquitous in largeundergraduate programming courses, and recent work has focused on improving thequality of automatically generated feedback. However, there is a relative lackof data directly comparing student outcomes when receiving computer-generatedfeedback and human-written feedback. This paper addresses this gap by splittingone 90-student class into two feedback groups and analyzing differences in thetwo cohorts' performance. The class is an intro to AI with programming HWassignments. One group of students received detailed computer-generatedfeedback on their programming assignments describing which parts of thealgorithms' logic was missing; the other group additionally receivedhuman-written feedback describing how their programs' syntax relates to issueswith their logic, and qualitative (style) recommendations for improving theircode. Results on quizzes and exam questions suggest that human feedback helpsstudents obtain a better conceptual understanding, but analyses found nodifference between the groups' ability to collaborate on the final project. Thecourse grade distribution revealed that students who received human-writtenfeedback performed better overall; this effect was the most pronounced in themiddle two quartiles of each group. These results suggest that feedback aboutthe syntax-logic relation may be a primary mechanism by which human feedbackimproves student outcomes.

Effects of Human vs. Automatic Feedback on Students' Understanding of AI Concepts and Programming Style